Identification of human response to virtual 3D face stimuli

Abstract

This paper introduces identification results of human response to virtual 3D face stimuli. Observations of human reactions are done using preprocessed EEG (electroencephalogram) signals: excitement, meditation, frustration, engagement/boredom. Virtual 3D face features – distance between eyes, nose width, and chin width – are used as stimuli. Cross-correlation analysis demonstrated that dynamical relations between human reactions and stimuli exist. Input-output models describing relations between stimuli and corresponding human reactions are built. A new input-output model building method is proposed that allows building stable models with the least output prediction error. Models’ validation results demonstrate relatively high prediction accuracy of human reactions.